Using Venv for Virtual Environments

This is a brief tutorial on how to use venv to create and manage python virtual environments.

Managing Python Environments with `venv`

This tutorial covers how to:

Create a virtual environment (from terminal and Positron)
Activate and deactivate it
Create a requirements.txt
Recreate an environment from requirements.txt

Why Virtual Environments?

In R, each project can effectively have its own package library. In Python, we achieve this using virtual environments.

A virtual environment:

Is isolated from your system Python
Has its own installed packages
Prevents version conflicts across projects

Virtual environments are particularly useful when working with smaller community- and single-maintainer packages which might rely on earlier versions of common libraries (numpy, pandas, etc).

1. Create a Virtual Environment

From the Terminal

Navigate to your project folder:

cd path/to/your/project

Create a virtual environment:

python -m venv .venv

This creates a folder called .venv inside your project.

If you have multiple Python versions

You may need:

python3.10 -m venv .venv

or on Windows:

py -3.10 -m venv .venv

From Positron

Open your project.
Open the Terminal pane.
Run:

python -m venv .venv

Select the interpreter:
- Command palette → “Python: Select Interpreter”
- Choose the interpreter inside .venv

Positron will now use that environment for the project.

Open your project.
Open Command pane (ctrl+shift+p or cmd+shift+p)
Select Python: Create Environment
Select venv
- Positron will create a virtual environment .venv for you

2. Activate and Deactivate a Virtual Environment

Activate

macOS / Linux

source .venv/bin/activate

Windows (PowerShell)

.venv\Scripts\Activate.ps1

Windows (Command Prompt)

.venv\Scripts\activate.bat

When activated, your terminal will show:

(.venv) $

Positron

Select the interpreter:

Command palette → “Python: Select Interpreter”
Choose the interpreter inside .venv

Deactivate

From any system:

deactivate

This returns you to your global Python.

3. Create a `requirements.txt`

After installing packages inside your virtual environment:

pip install numpy pandas scikit-learn

Freeze the environment:

pip freeze > requirements.txt

This creates a file like:

numpy==1.26.4
pandas==2.2.1
scikit-learn==1.4.0

This captures exact versions — similar to renv::snapshot() in R.

4. Recreate an Environment from `requirements.txt`

If you clone a repo and it has a requirements.txt file:

Step 1: Create a new virtual environment

python -m venv .venv

Step 2: Activate it

(macOS/Linux)

source .venv/bin/activate

(Windows PowerShell)

.venv\Scripts\Activate.ps1

Step 3: Install from requirements

pip install -r requirements.txt

This installs all specified packages and versions.

Equivalent idea in R:

renv::restore() ≈ pip install -r requirements.txt

Recommended Project Structure

project/
│
├── .venv/
├── requirements.txt
├── analysis.py
└── README.md

If using git, add .venv/ to .gitignore so it is not committed!

Managing Python Environments with venv